Platinum-functionalized graphene combines ultra-high carrier mobility with Pt’s catalytic activity for chemiresistive H₂ detection, but connecting synthesis, morphology, and sensor metrics at the atomistic level is challenging. We trained an equivariant neural network potential with near-DFT accuracy to run large-scale MD simulations of Pt PVD crystal growth on graphene and subsequent H₂ sensing, establishing a predictive pipeline from deposition conditions through nanocluster structure to chemiresistive performance.
This repository provides:
- Equivariant NNP workflows for training and deploying machine-learned interatomic potentials.
- MD pipelines for Pt physical-vapor-deposition (PVD) crystal growth on graphene.
- H₂-sensing simulations on Pt/graphene devices.
- Tools to compute key figures of merit:
- Response time & Recovery time
- Limit of detection
- Transduction sensitivity
Interactive, browser-based video visualizations (no software install required — Just Click):
Data-Driven Molecular Dynamics and TEM Analysis of Crystal Growth and Hydrogen Sensing in Platinum-Functionalized Graphene Chemiresistive Sensors
arXiv: 2504.05438